The classification of wines, e.g., according to sort of wine, growing region, grape, and vintage, is at present only possible by the sensory subjective way through the excellently trained sense of smell and taste of a wine connoisseur. Besides the inaccuracies inherent in nature such as, e.g., in the differentiation of single vintages of a wine, these sensory qualities are limited to only a relatively small number of persons.
Accordingly attempts have not been scarce to examine the various wines by scientific methods, for instance the analysis of single chemical parameters such as sugar content, acidity, ethanol content etc., and/or by physico-chemical methods such as optical rotary dispersion, index of refraction etc., and achieve a classification as named at the outset through interpretation of the single data, a group of data, or the entirety of such data.
Owing to the complex composition of the wine on the one hand, but on the other hand the similarity of single parameters, any attempts at giving a reliable statement with the aid of analytical methods, e.g. about sort of wine, growing region, grape and vintage of a wine in question, have failed up to the present.
However, for various reasons it is sensible to have at one's disposal a reliable method for classifying wines. For one thing, monitoring trade products in terms of food technology and their conformity with statutory requirements is hereby possible, for it is possible to detect, e.g., whether the criteria of designation of the growing region are complied with, such as, e.g., whether or not an inadmissible blending with another grape/growing location exists. On the other hand, monitoring the production process and maturation in storage in the course of wine production by the wine grower would be conceivable with such a classification system.
Apart from the above mentioned classical prior art of wine analytics it is moreover known from the graduation thesis entitled, “Anwendung multivariater Methoden und kunstlicher neuronaler Netze zur Klassifizierung von Spirituosen mittels Headspace-GC/MS-Kopplung”, presented by Patrick Kursawe, Chair for Analytical Chemistry of Ruhr-Universitat Bochum 1998, to classify various spirits ranging from grappa to rum, with relative reliability by applying multivariate methods and artificial neuronal networks and principal components analysis.
With regard to classification of wine by the modern chemometrical methods, Montanarella et al. (Montanarella, T., Bassani, M. R., Broas, O. (1995): Chemometric Classification of Some European Wines Using Pyrolysis Mass Spectrometry, Rapid Comm. Mass Spectrom. 9 (15), 1589-1593] have attempted, by utilizing various multivariate methods as well as backpropagation networks, to classify wines with regard to their country of origin with the aid of pyrolysiso mass spectra. A fine differentiation between different regions did, however, fail.